Novel Application of Query-Based Qualitative Predictors for Characterization of Solvent Accessible Residues in Conjunction with Protein Sequence Homology

Rose, Daniel A.; Nepal, Reecha; Mishra, Radhika Pallavi; Lau, R; Gholizadeh, S; Lustig, Brooke

doi:10.1109/dexa.2011.57

Cited by 1 publication

(4 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We continue with simple approaches to parsing homology data, noting that requiring at least ten aligning BLASTp subject sequences given the 40% threshold relative to the highest bit score is a reasonable condition for fully reliable sequence entropies (Liao et al, 2005;Rose et al, 2011). We validate our models using two test sets, where one standard test set, Manesh-215 (Naderi-Manesh et al, 2001), has been thoroughly evaluated with respect to many standard RSA approaches (Nguyen & Rajapske, 2006).…”

Section: Data Assemblymentioning

confidence: 99%

“…2. The learning set corresponds to all 18 available transient-binding proteins from our original 268 learning set (Mishra, 2010;Rose et al, 2011) and current 1363-based learning set. These transient-binding proteins were originally characterized as such by Pettit et al (2007).…”

Section: Learning and Test Setsmentioning

confidence: 99%

“…Also, even the simpler E6+AA model performs nearly as well for both the 20% and the alternative 25% RSA threshold. In both the multiple linear regression (Rose et al, 2011;Nepal, 2013) and logistic models, E6 appears to be the more reliable predictor of residue solvent accessibility as opposed to E20 (see Table 3). This is consistent with the notion that E6 entropy is the more sensitive measure of residue packing (Mirny & Shakhnovich, 2001).…”

Section: Generalmentioning

confidence: 99%

“…Following our previous work (Rose et al, 2011;Nepal, 2013), we include a qualitative descriptor representing query residue type (AA) as well as four homology descriptors: 20-term (E20) and six-term (E6) sequence entropy, fraction of aligned residues that are strongly hydrophobic (FSHP), and fraction of small residue glycine or alanine (FSR). The AA descriptor alone has been shown to be a significant predictor ($70%) of residue solvent accessibility.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Logistic regression models to predict solvent accessible residues using sequence- and homology-based qualitative and quantitative descriptors applied to a domain-complete X-ray structure learning set

et al. 2015

Self Cite

View full text Add to dashboard Cite

A working example of relative solvent accessibility (RSA) prediction for proteins is presented. Novel logistic regression models with various qualitative descriptors that include amino acid type and quantitative descriptors that include 20-and six-term sequence entropy have been built and validated. A domain-complete learning set of over 1300 proteins is used to fit initial models with various sequence homology descriptors as well as query residue qualitative descriptors. Homology descriptors are derived from BLASTp sequence alignments, whereas the RSA values are determined directly from the crystal structure. The logistic regression models are fitted using dichotomous responses indicating buried or accessible solvent, with binary classifications obtained from the RSA values. The fitted models determine binary predictions of residue solvent accessibility with accuracies comparable to other less computationally intensive methods using the standard RSA threshold criteria 20 and 25% as solvent accessible. When an additional non-homology descriptor describing Lobanov-Galzitskaya residue disorder propensity is included, incremental improvements in accuracy are achieved with 25% threshold accuracies of 76.12 and 74.79% for the Manesh-215 and CASP(8+9) test sets, respectively. Moreover, the described software and the accompanying learning and validation sets allow students and researchers to explore the utility of RSA prediction with simple, physically intuitive models in any number of related applications.

show abstract

Section: Data Assemblymentioning

confidence: 99%

Section: Learning and Test Setsmentioning

confidence: 99%